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Looking for DevOps Engineer (m/f/d)

We are seeking a skilled DevOps Freelancer to maintain and enhance our production systems and software.

Responsibilities:
-Manage and optimize our cloud and platform infrastructure using Azure and Kubernetes
-Automate deployments and infrastructure using CI/CD pipelines (GitLab, GitHub, Terraform, Helm)
-Monitor and maintain databases (MySQL, PostgreSQL, Weaviate)
-Ensure system stability through monitoring & observability with Prometheus, Grafana, and Loki
-Manage authentication and access control using KeyCloak
Work with NginX and Traefik for routing and load balancing
-Debug and resolve issues, while also developing minor features to enhance system functionality

Must-Have Skills:
-Cloud & Platform: Experience with Azure, Kubernetes, and container orchestration
-CI/CD: Proficiency in GitLab Pipelines (preferably with NX integration), GitHub, and Azure Pipelines
Infrastructure as Code (IaC): Hands-on experience with Terraform and Helm
-Security & Access: Strong knowledge of VPN and SSH for connecting to customer systems
-Docker: Solid experience with containerization and service management
-Monitoring & Observability: Familiarity with Prometheus, Grafana, Loki for system monitoring
-Databases: Strong expertise in MySQL, PostgreSQL

Nice-to-Have Skills:
-Experience with Weaviate as a vector database
-Familiarity with Azure Pipelines for CI/CD

• Please state the specific hours you are available to work in CET timezone (e.g., 9 AM - 5 PM CET, Monday-Friday)
• Do you agree to take part in a short assessment so we can get to know your skills?
• Do you have knowledge of Terraform and Helm? What about Docker?
• Are you familiar with Prometheus, Grafana, Loki,Azure and Kubernetes?
• Do you have strong expertise with MySQL and PostgreSQL?

requirements for the AI Evaluation & Voice Testing Platform,
Phase 1 — Voice Load Testing & Core Evaluation Platform

This phase will include:

• Test Suite Dashboard (create/manage evaluation suites)
• SIP / API / Webhook connection modes
• Voice load testing framework (SIPp based)
• Concurrent call simulation (demo scale locally, scalable to 3000 ports on server)
• Deterministic flows (scripted IVR tests)
• Agentic flows using LLM for dynamic conversations
• Retry logic for failed calls
• Technical metrics collection (latency, success rate, call failures)
• Basic reporting dashboard

Phase 2 — AI Evaluation Engine & Red Teaming

Includes:

• AI evaluation scoring (intent accuracy, entity extraction)
• Hallucination detection
• Prompt-injection / red-team testing scenarios
• AI judge using LLM (OpenAI / Vertex AI / Bedrock)
• Conversation transcript analysis
• Evaluation scorecards per test run

Advanced Observability & Reporting

Includes:

• Grafana / Datadog integration
• Full analytics dashboards
• Test result comparison between versions
• Performance regression detection
• Exportable reports for stakeholders


We can start with Phase 1 (Voice Load Testing + Core Evaluation) and expand the platform gradually.

Skills: VoIP, Artificial Intelligence, SIP, Natural Language Processing, AI Development, AI Agents

We are looking for an experienced k6 / Grafana Synthetic Monitoring expert to help us implement a working browser-based synthetic test for our application.

Our goal is to simulate a simple user journey using Grafana Cloud Synthetic Monitoring (k6 browser), but despite multiple attempts the test keeps failing during the login flow.

This should be straightforward for someone with strong k6 browser automation experience.
We want a synthetic browser test that validates user journey.

Current Problem
The script consistently fails at the password step.
Example error:
waiting for selector "#password": timed out after 30000ms

We have already tried:
Grafana k6 Studio recording
HAR recording
Writing manual k6 browser scripts
Handling redirects and waits
The generated scripts are extremely large (thousands of lines) and include dynamic tokens that expire.

What We Need
We need an expert to:
• Write a clean working k6 browser script
• Handle the OAuth redirect login flow
• Ensure the script runs reliably in Grafana Cloud Synthetic Monitoring
• Keep the script simple and maintainable
Deliverables
Working k6 browser synthetic script
Short explanation of selectors and waits

Instructions to deploy in Grafana Cloud Synthetic Monitoring
Required Skills
k6
Grafana Cloud Synthetic Monitoring
Browser automation
OAuth login flows
SPA applications (Angular / React)

Nice to Have
Experience with:
Grafana k6 browser module
Synthetic monitoring tools (Grafana / Datadog / New Relic)
Playwright or Puppeteer

Additional Context
We previously used New Relic Synthetics, where defining steps through the UI worked easily. We are trying to implement the same monitoring flow using Grafana Cloud but have not yet succeeded.
We already have partial scripts and recordings that can be shared with the freelancer.

Budget
Open to fixed price or hourly depending on experience.
We expect this to be a small task for someone familiar with k6 browser testing.

Necesitamos un DevOps arquitecto en la nube, con 10+ años de experiencia. Su experiencia debe ser comprobable en OVH Cloud y otros.

Requisitos:

Habilidades técnicas.....
Gestión de infraestructura: Conocimiento en herramientas como Terraform, Ansible y Kubernetes.

Automatización: Dominio de scripts y lenguajes como Bash, Python y PowerShell.

Control de versiones: Experiencia con Git y plataformas como GitHub o GitLab.

Monitoreo y observabilidad: Manejo de Prometheus, Grafana y herramientas de logging como ELK Stack.

Seguridad: Implementación de políticas de seguridad y gestión de acceso (IAM, Firewalls, Zero Trust).

Habilidades blandas
Colaboración: Trabajo en equipo con desarrolladores, operadores y stakeholders.

Comunicación efectiva: Explicar conceptos técnicos a equipos no técnicos.

Resolución de problemas: Detectar y solucionar incidentes de manera rápida y eficiente.

Mentalidad de mejora continua: Búsqueda constante de optimización de procesos.


Con preferencia, sea Cristiano. Le esperamos.

Skills: System Admin, Linux, Software Architecture, PostgreSQL, Ubuntu, Virtual Machines, Terraform

We're looking for a Middle+ DevOps / Site Reliability Engineer to join our team for a long-term engagement (3+ months).

Location: Fully remote – GMT+8 time zone overlap required
English: C1/C2 Fluent

What you'll be doing:
• Owning reliability, availability, and performance of production environments
• Leading incident response end-to-end: troubleshooting, mitigation, resolution, and RCA
• Operating production Kubernetes (EKS), including cluster upgrades and Helm deployments
• Managing scaling and capacity with KEDA, Karpenter, and HPA
• Evolving infrastructure as code using Terraform and Helm
• Supporting GitLab CI/CD pipelines and improving deployment stability
• Designing observability systems with Prometheus, Grafana, and EFK
• Solving networking issues: TLS, Load Balancing, VPCs, NAT, VPN
• Participating in on-call rotations for consistent operational coverage

Must have:
• 3+ years of hands-on DevOps / SRE experience
• Strong production experience with Docker and Kubernetes
• Solid AWS knowledge: EKS, EC2, IAM, RDS, S3, CloudWatch, Lambda
• Monitoring, logging, and alerting systems
• Terraform, Helm, GitLab CI
• Scripting: Bash and Python
• Pub/sub systems: SQS, RabbitMQ, Kafka or similar
• Fluent English + willingness to participate in on-call rotations

Nice to have:
• Redis, Airflow, Databricks, Spark/EMR
• GitOps workflows and advanced Git usage
• Databases: Postgres, Snowflake, ClickHouse
• Proficiency in Mandarin
• Experience with AI-powered dev tools

Sounds like a fit? Send us your CV, expected rate, and a couple of sentences about your DevOps / SRE experience – we're looking forward to seeing your expertise in action!

PostgreSQL Database Architect / DevOps Engineer (Scaling & Infrastructure)

We are looking for an experienced Database Architect / DevOps Engineer to help improve and scale our PostgreSQL infrastructure. Our system is already live and functional, but it has grown organically and now needs architectural refinement, stronger operational practices, and a clear scaling strategy.

Current Architecture

Application Database
Supabase (PostgreSQL) managing authentication, user data, and application tables for a Next.js SaaS product hosted on Vercel.

Data Pipeline Databases
Four separate PostgreSQL instances running in Docker containers on a dedicated Unraid/Linux server. These databases support large datasets, enrichment pipelines, and bulk data processing workflows. One of the primary databases currently holds 18M+ records.

Connection Pooling
pgbouncer is used to manage connections across the PostgreSQL instances.

Processing Pipelines
Node.js and Python services running in Docker containers and PM2 perform data enrichment, email verification, and processing workflows.

Infrastructure
All services run in Docker containers on a bare-metal server with no orchestration layer currently in place.

What We Need Help With

Architecture Review
Evaluate our multi-database setup and recommend improvements such as consolidation, schema separation, or other architectural optimizations.

Performance Optimization
Improve indexing, query performance, partitioning strategies, and database maintenance for large datasets (18M+ rows).

Operational Maturity
Implement automated backups, monitoring, alerting, disaster recovery planning, and reliable database migration workflows.

Scaling Strategy
Design a clear path for scaling from ~20M records to 100M+ records across our data systems.

Docker & Infrastructure
Improve container configuration, networking, logging, health checks, and overall infrastructure reliability.

Connection Management
Review and optimize our pgbouncer configuration for current workload patterns.

Pipeline Reliability
Advise on building reliable data processing pipelines including retry logic, idempotency, and queue-based processing patterns.

Required Skills
5+ years of PostgreSQL administration, performance tuning, and architecture experience.
Strong production experience with Docker.
Experience scaling large PostgreSQL datasets using partitioning, replication, or sharding strategies.
Experience with pgbouncer or similar connection pooling tools.
Deep understanding of indexing strategies for large datasets (B-tree, GIN, partial indexes).
Experience implementing backup and disaster recovery strategies for PostgreSQL.
Experience setting up monitoring and alerting (pg_stat, Prometheus, Grafana, or similar).

Nice to Have
Experience with Supabase or managed PostgreSQL environments.
Experience designing ETL or data processing pipelines.
Familiarity with Node.js or Python services.
Experience managing bare-metal Linux infrastructure.
Experience scaling systems from single-server deployments to distributed architectures.

• You inherit an 18M-row PostgreSQL table with 20 indexes (~15 GB, nearly 1:1 with data), serving both real-time filtered searches (trigram + full-text) and batch writes (~210 rows/min). What's your approach to evaluating and improving this?*
• We run 4 PostgreSQL instances in Docker on bare metal — no orchestration, no automated backups, no monitoring. What do you prioritize in the first 2 weeks to move toward production-grade reliability?
• Our enrichment pipelines process millions of records but have no DLQ, no retry logic, and failures are only caught via logs. How would you improve pipeline reliability at the database level without over-engineering?
• Describe your recent experience with similar projects

I have a fresh VPS waiting for a smart, fully automated multi-agent stack that covers both social media and SEO. Here’s exactly what I’m after:

• Social agents must handle post scheduling, pull useful analytics reports, and keep engagement flowing through automated or semi-automated messaging on Facebook, Instagram, TikTok, and YouTube.
• SEO agents should run keyword research, perform on-page checks/fixes, and help initiate or monitor backlink campaigns.

I’m platform-agnostic as long as the tools are reliable—Python, Node, Docker containers, LangChain, or any comparable framework is fine. Once installed, the system should be accessible via a simple browser dashboard (Grafana, Superset, Metabase, or your preferred UI) so I can track social queues, SEO audits, and performance metrics in one place.

Acceptance criteria
1. Agents fully deployed on my VPS (Ubuntu 22.04).
2. Dashboard shows scheduled posts, engagement inbox, analytics charts, keyword lists, on-page audit scores, and backlink status.
3. Clear README covering architecture, environment variables, cron jobs, and how to add new social accounts or websites.
4. A brief hand-off call or video walkthrough ensuring everything runs smoothly after you log out.

If you’ve built similar automation, I’d love to see a demo or repo link. Let’s get this running quickly—I’m ready to hand over SSH access and any API keys you’ll need.

Skills: Python, Linux, SEO, Ubuntu, Docker, Social Media Management, Automation, LangChain

You will be required to take a coding and systems logic test. It will be very hard to pass using AI, unless you truly understand the subject matter and are an expert in the fields described below:

We're an AI-powered content distribution platform.

Behind the scenes, this means: distributed device infrastructure, complex proxy/VPN networks, multi-provider data scraping pipelines, real-time metric reconciliation, and dashboards that brands rely on for decision-making. When our data is wrong, our clients make bad decisions. When our infrastructure breaks, content stops publishing.

We need an engineer who treats every failure as a data integrity problem and every script as production code.

What You'll Own
Data Pipeline & Integrity

End-to-end ownership of the scrape-to-dashboard data pipeline: ingestion, normalization, reconciliation, storage, and reporting
Metric reconciliation across multiple scraping providers (Apify, custom scrapers), including mismatch resolution, deduplication, merge conflicts, and provider failover

Historical data persistence. Designing and maintaining immutable snapshot tables so all-time metrics never silently degrade

Post-matching logic: URL normalization across platforms (mobile vs desktop vs share URLs, redirects, query params), caption-based fuzzy matching as fallback, platform post ID linkage
Attribution integrity. Ensuring metrics stay tied to the correct client, agent, and account through reassignments, rotations, and account migrations

Data model health: orphan detection (posts without queue rows, queue rows without agents), relationship integrity checks, cascade handling

Building observability into every pipeline stage. Monitoring, alerting, and anomaly detection so broken pipelines never go unnoticed

API & Backend Systems

REST and/or GraphQL API design and maintenance for internal services and client-facing dashboards

Queue/worker architecture (Redis + Bull/BullMQ or equivalent). Ensuring every enqueued task completes, failures retry correctly, and partial runs are flagged

Scheduler reliability: cron jobs, task orchestration, environment parity between manual and automated runs

Database design and optimization in PostgreSQL: schema evolution, indexing strategy, query performance, migration safety
Third-party API integration management: handling rate limits, schema changes, authentication rotation, provider failover logic
Webhook and event-driven architecture for real-time data sync between services

Device Infrastructure & Automation

Automation scripts for configuring and validating a distributed fleet of Android devices at scale

Anti-detection engineering: VPN/proxy architecture, device fingerprint management, network configuration validation
Device compliance systems. Automated checks for locale, timezone, IP, carrier status, app versions, naming conventions, and security settings

Account health monitoring, ban rate analysis, and mitigation strategies

Coordination with distributed on-site hardware teams across timezones

Quality & Reliability Engineering

Every script tested against real-world device and data states before production deployment

Sandboxed rollouts. Small batch validation before fleet-wide execution

Automated regression testing for known failure modes across the full pipeline

Version pinning for app packages, OS versions, script versions, and provider schemas

CI/CD pipeline maintenance and deployment safety (canary deploys, rollback procedures)


Required Technical Skills
Core Stack

PostgreSQL: advanced (schema design, window functions, CTEs, index optimization, migration strategies, JSONB handling)
Redis: caching patterns, pub/sub, queue backends (Bull/BullMQ or similar)

Node.js and/or Python: production scripting, async patterns, error handling at scale

REST API design: versioning, authentication, rate limiting, error contracts

Git: branching strategies, code review, CI/CD integration

Data Engineering

ETL pipeline design: extraction from flaky third-party sources, transformation with validation, loading with idempotency
Data reconciliation and deduplication across multiple sources with conflicting schemas

Time-series data handling: snapshots, monotonic metric enforcement, backfill strategies

Schema evolution and migration patterns without downtime
Data quality monitoring: drift detection, anomaly alerting, completeness checks

Infrastructure & Automation

Linux systems administration: networking, DNS, firewall rules, process management

VPN/proxy architecture: debugging routing, DNS leakage, IP validation

ADB and Android device management (or equivalent embedded/IoT fleet management)

Container orchestration basics (Docker, or willingness to learn)
Monitoring stack experience (Grafana, Prometheus, Datadog, or similar)

Testing & Quality

Test-driven development: unit, integration, and end-to-end testing patterns

Edge case identification as a core engineering discipline, not an afterthought

Defensive programming: input validation, graceful degradation, fail-loud design

Load testing and performance profiling for APIs and data pipelines


You're a Strong Fit If

You have 4+ years building backend systems, data pipelines, or infrastructure automation

You've worked with physical or cloud device management at scale (BrowserStack, AWS Device Farm, Sauce Labs, mobile CI/CD, IoT fleet management)

You instinctively think about what breaks before what works
You've debugged silent data drift, the kind where numbers look plausible but are quietly wrong

You're comfortable owning a system end-to-end: from the cron job that triggers it, to the worker that processes it, to the database that stores it, to the dashboard that displays it
You write tests that cover the cases nobody thought of, not just the happy path

You communicate clearly in writing. Design docs, incident postmortems, code comments that explain "why"

Bonus Points

Experience with social media platform APIs and data quirks (TikTok, Instagram, YouTube)

Scraping infrastructure experience (Apify, Puppeteer, Playwright, headless browsers)

Experience building internal tools and admin dashboards (React preferred)

Background as an SDET, QA Platform Engineer, or Reliability Engineer

Familiarity with content distribution, influencer marketing, or creator economy platforms


How We Work

Async-first with overlap during US business hours
Direct access to the CEO. Zero management layers
Full ownership and autonomy over your domain
We ship fast but we ship right. "Works on my machine" isn't acceptable when it needs to work at scale across devices and data pipelines

I have a fresh VPS waiting for a smart, fully automated multi-agent stack that covers both social media and SEO. Here’s exactly what I’m after:

• Social agents must handle post scheduling, pull useful analytics reports, and keep engagement flowing through automated or semi-automated messaging on Facebook, Instagram, TikTok, and YouTube.
• SEO agents should run keyword research, perform on-page checks/fixes, and help initiate or monitor backlink campaigns.

I’m platform-agnostic as long as the tools are reliable—Python, Node, Docker containers, LangChain, or any comparable framework is fine. Once installed, the system should be accessible via a simple browser dashboard (Grafana, Superset, Metabase, or your preferred UI) so I can track social queues, SEO audits, and performance metrics in one place.

Acceptance criteria
1. Agents fully deployed on my VPS (Ubuntu 22.04).
2. Dashboard shows scheduled posts, engagement inbox, analytics charts, keyword lists, on-page audit scores, and backlink status.
3. Clear README covering architecture, environment variables, cron jobs, and how to add new social accounts or websites.
4. A brief hand-off call or video walkthrough ensuring everything runs smoothly after you log out.

If you’ve built similar automation, I’d love to see a demo or repo link. Let’s get this running quickly—I’m ready to hand over SSH access and any API keys you’ll need.

Skills: Python, Linux, SEO, Ubuntu, Docker, Social Media Management, Automation, LangChain

Grafana Dashboard Integration into React App for UCaaS Platform
Description:
We need to migrate/integrate an existing Perplexity AI-generated dashboard into a self-hosted Grafana setup, then embed it seamlessly into my React-based UCaaS app. No ongoing third-party dependencies—everything runs on my server with real-time data connections (e.g., DB/APIs, including Gmail/Drive if needed).
Requirements:
Export assets from Perplexity Labs/Computer mode (I'll provide access).
Set up Grafana OSS via Docker on my VPS.
Rebuild dashboard in Grafana with real-time features.
Embed Grafana panels/dashboards in React using iframes or custom components (handle auth/styling).
Ensure independence: No Perplexity lock-in.
Skills: Grafana, React.js, Docker, APIs (Google/OAuth).
Deliver: Code handover, docs, and testing.

Department: Engineering / Product Development
Reports To: Tech Lead / Engineering Manager

About Swyftbooking
Swyftbooking is a fast-growing travel technology platform that powers seamless booking experiences for travelers and partners. We leverage leading airline, hotel, and content aggregators through robust API integrations to deliver reliable, scalable, and delightful user experiences. We are looking for a hands-on Full-Stack Developer to strengthen our API integration capabilities, particularly with Mondee and Amadeus, and to own end-to-end delivery of features that impact both partners and end users.

Role Overview
As a Full-Stack Developer focused on API integration, you will architect, implement, test, and maintain integrations with Mondee and Amadeus APIs, while also contributing to the broader Swyftbooking product. You will collaborate closely with Product, Partnerships, and Platform teams to translate business requirements into scalable technical solutions. You should be comfortable working across the stack—from backend services and microservices to frontend interfaces and developer tooling.

Key Responsibilities


Design, develop, and maintain robust integrations with Mondee and Amadeus APIs, including search, availability, pricing, inventory, bookings, and post-booking activities.

Build and maintain reliable, scalable backend services (APIs, microservices) that consume, normalize, and cache data from external partners; implement retry, circuit breaker, and paging strategies as needed.

Implement secure authentication/authorization flows for partner integrations (API keys, OAuth, token management) and ensure compliance with partner SLA requirements.

Create GraphQL/REST endpoints to support frontend needs and partner-facing integrations; design data models to normalize third-party responses.

Develop front-end features (React, Vue, or Angular) to present search results, fare rules, booking flows, and partner-specific content with responsive UI/UX.

Write clean, well-documented code and maintain comprehensive unit/integration tests (Jest, Mocha, PyTest, etc.); implement end-to-end tests for critical flows.

Monitor, log, and instrument integrations; implement observability (metrics, traces, dashboards) to ensure reliability and quick issue resolution.

Collaborate with Product and Partnerships to define acceptance criteria, success metrics, and roadmap for API integration capabilities.

Participate in code reviews, architectural discussions, and knowledge-sharing sessions.

Stay up-to-date with Mondee/Amadeus API changes and industry best practices; proactively plan migrations or adjustments as APIs evolve.

Ensure data quality, deduplication, and handling of edge cases (multi-currency, tax rules, regional pricing).

Contribute to DevOps practices: CI/CD pipelines, containerization (Docker), and cloud deployments (AWS/Azure/GCP).

Required Qualifications


3+ years of professional experience as a Full-Stack Developer or similar role.

Demonstrated hands-on experience integrating with Mondee and/or Amadeus APIs (search, availability, pricing, bookings, post-booking) in a production environment.

Strong backend experience (Node.js/Express, Python/Dast, Java/Spring, or equivalent) and API design (REST/GraphQL).

Frontend experience with modern frameworks (React, Angular, or Vue) and state management.

Experience with databases (PostgreSQL/MySQL), ORM/ODM, and data modeling for third-party data.

Familiarity with authentication/authorization (OAuth 2.0, API keys) and security best practices.

Knowledge of caching, messaging queues (RabbitMQ/Kafka), and asynchronous processing.

Proficiency with containerization and cloud deployments (Docker, Kubernetes, AWS/GCP/Azure).

Strong problem-solving skills, with the ability to translate business requirements into technical solutions.

Excellent communication and collaboration skills; comfortable working with cross-functional teams and external partners.

Nice-to-Haves


Prior experience with travel tech platforms, GDS/OTA systems, or fare comparison engines.

Knowledge of fare rules, tax calculation, currency handling, and regulatory requirements in travel booking.

Experience with event-driven architectures and API gateway management.

Familiarity with test automation, contract testing (PACT), and API performance testing.

Knowledge of monitoring/observability tools (Prometheus, Grafana, OpenTelemetry, ELK/EFK stacks).

Experience with serverless architectures (AWS Lambda) or microservices patterns.

Soft Skills


Customer-centric mindset with attention to detail.

Proactive learner who stays current with API ecosystem changes.

Agile mindset with the ability to adapt to evolving priorities.

Strong ownership and accountability for deliverables.

What We Offer


Competitive salary and equity package (depending on location and experience)

Flexible work arrangements (remote-friendly, with optional in-person collaboration)

Comprehensive benefits (health, dental, vision, 401(k) or equivalent)

Learning and growth opportunities (conference attendance, training budgets)

Modern tech stack and culture that values quality, autonomy, and collaboration

Metrics and Success Indicators


Reliability and uptime of API integrations (SLA adherence)

Timeliness of feature delivery and milestone completion

Code quality and test coverage

Partner satisfaction and reduction in integration-related tickets

Observability and incident response improvements

Hiring Process



Short phone screen (20-30 min)




Technical take-home exercise focused on Mondee/Amadeus-like API integration or a 2-hour whiteboard/arch design session

We are developing a distributed computing and automation infrastructure designed to coordinate workloads across multiple lightweight execution environments connected to a central orchestration system.
The goal of this project is to build a scalable platform capable of managing remote worker environments, executing tasks in parallel, and coordinating multiple processes within a distributed architecture.
Rather than building every component from scratch, the system will integrate several mature open-source technologies into a cohesive infrastructure. The focus of the project is on system architecture, integration, and scalability.
The platform will include components such as:
• task orchestration
• remote worker management
• distributed task execution
• fault tolerance mechanisms
• system monitoring and observability
• automation infrastructure
The system will be designed to distribute workloads across multiple execution environments, allowing different processes to run simultaneously in a coordinated way.
________________________________________
High-Level Architecture
The architecture will include:
• a central orchestration and coordination system
• multiple remote execution environments (workers)
• distributed task scheduling and workload management
• lightweight virtualized environments
• monitoring and system observability
• failure recovery mechanisms
The system should be capable of:
• assigning tasks across multiple workers
• distributing workloads between different execution environments
• recovering tasks if an execution environment becomes unavailable
• monitoring the overall health of the infrastructure
________________________________________
Expected Technology Stack
The platform will primarily use open-source technologies such as:
• Python
• Ray (distributed computing framework)
• Nomad or Kubernetes (orchestration layer)
• Firecracker (micro-VM technology)
• Redis / PostgreSQL
• Prometheus / Grafana (monitoring and metrics)
• Linux-based infrastructure tools
The selected engineer will also help refine the architecture and recommend improvements to the technology stack when appropriate.
________________________________________
Who We Are Looking For
We are looking for one or two senior engineers with strong experience in distributed systems and infrastructure development.
The ideal candidate should be capable of:
• designing distributed system architectures
• integrating multiple open-source technologies
• building scalable backend infrastructure
• working with Linux systems and networking
• implementing robust monitoring and fault tolerance mechanisms
This role involves building and integrating a full platform, not isolated small tasks.
________________________________________
Responsibilities
Responsibilities will include:
• designing the overall system architecture
• integrating multiple open-source components
• implementing orchestration and worker management systems
• building distributed task execution mechanisms
• implementing monitoring and observability tools
• ensuring system reliability and scalability
________________________________________
Technical Requirements
Strong experience with:
• Python
• Linux systems
• distributed systems concepts
• networking fundamentals
• backend architecture
Experience with the following technologies is highly valued:
• Ray
• Nomad
• Kubernetes
• Firecracker
• Redis
• PostgreSQL
• Prometheus / Grafana
________________________________________
Ideal Candidate
We are looking for engineers who have experience building:
• distributed platforms
• cloud infrastructure systems
• automation infrastructure
• advanced backend systems
Experience designing complex infrastructure or platform-level systems is strongly preferred.
________________________________________
Project Scope
The project will be developed in several phases:
1. system architecture design
2. core infrastructure implementation
3. integration of remote execution environments
4. distributed task management
5. monitoring, reliability, and scalability improvements
Depending on progress and results, this may become a long-term collaboration.
________________________________________
How to Apply
Please include in your proposal:
1. your experience with distributed systems
2. examples of infrastructure or platform projects you have worked on
3. technologies you are most comfortable with
4. how you would approach the architecture of a system like this

Overview
We are looking for a senior DevOps contractor based in the United States to support our engineering team for the next ~60 days (full-time, 40 hours/week).

This role is focused on CI/CD automation, preview environment infrastructure, monitoring/observability, and DevOps platform improvements across a multi-repository microservices environment.

Because we operate in a regulated environment, contractors will work on company-managed devices via VPN, so US-based candidates only will be considered.

We are looking for someone who can hit the ground running on day one, work independently, and quickly integrate with our DevOps workflows.

Key Responsibilities

You will work closely with our Head of DevOps to implement and enhance our deployment and observability infrastructure.

Typical tasks include:

- Implementing deployment workflows across ~27 service repositories
- Provisioning frontend preview environments using AWS services
- Creating and managing DNS records for preview routing
- Automating GitHub environments, branch protections, and CODEOWNERS
- Integrating Sentry monitoring across deployment manifests
- Auditing Infrastructure-as-Code coverage and identifying gaps
- Writing production support runbooks and deployment playbooks
- Improving alerting and observability systems
- Building and enhancing Grafana dashboards for CI/CD and deployments
- Supporting CI workflows for test execution against preview environments

*** The engagement will primarily involve platform automation and DevOps infrastructure work, not application development.

Technical Environment

You should have strong experience with most of the following:

- Cloud & Infrastructure
- AWS
- Terraform
- EKS / Kubernetes
- Infrastructure-as-Code best practices
- CI/CD & DevOps
- GitHub Actions
- ArgoCD
- Multi-repo deployment automation
- Environment management
- Observability & Monitoring
- Grafana
- Prometheus
- OpenTelemetry
- PagerDuty
- New Relic
- GitHub
- Notion
- AI developer tools such as Cursor or Claude

You will have access to:

- GitHub
- Scoped AWS environments (no production write)
- Grafana
- PagerDuty
- New Relic

Requirements

Must be located in the United States

Available full-time (40 hours/week) for ~60 days

Senior-level DevOps experience (7+ years preferred)

Strong experience with AWS infrastructure automation

Experience managing CI/CD pipelines across many repositories

Comfortable writing runbooks, operational documentation, and playbooks

Able to work independently with minimal supervision

Nice to Have

Experience with preview environments for frontend deployments

Experience scaling observability infrastructure

Familiarity with AI-assisted development workflows

Experience working in regulated environments

Engagement Details

Duration: ~60 days

Hours: Full-time (40 hrs/week)

Location: United States only

Work setup: Company-managed device + VPN

ENTERPRISE AI DOCUMENT CHATBOT SYSTEM -- RAG ARCHITECTURE (Corporate Scale)

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PROJECT OVERVIEW
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We are building a production-grade AI chatbot system powered by Retrieval-Augmented Generation (RAG) for a organisation in Malaysia. The system must handle thousands of documents (PDFs, Word, Excel), live database queries, and serve two distinct user types -- public citizens and internal staff.

This is NOT a simple chatbot. This is an enterprise knowledge management system with strict data security, role-based access, bilingual support (Bahasa Malaysia + English), and full audit compliance.

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CORE REQUIREMENTS
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1. DOCUMENT INGESTION PIPELINE
- Support PDF, Word (.docx), Excel (.xlsx/.csv), and plain text files
- Handle 10,000+ documents efficiently with batch processing
- Automatic PII detection and redaction before embedding (Malaysian IC, phone numbers, emails, addresses)
- Smart chunking strategy (sentence-aware, 150-250 words per chunk with overlap)
- Document tagging system: public / internal / confidential
- Incremental ingestion -- only re-process changed or new documents
- Support for scanned PDFs via OCR (Tesseract or equivalent)
- Progress tracking and ingestion logs

2. VECTOR DATABASE AND RETRIEVAL
- Qdrant as primary vector database (self-hosted)
- Support for 1M+ vectors with fast retrieval (under 200ms)
- Hybrid search: semantic (vector) + keyword (BM25) combined scoring
- Metadata filtering by document tag, department, date range
- Re-ranking of retrieved chunks for accuracy
- Separate collections per document category
- Database integration: connect to PostgreSQL/MySQL and answer queries from live data

3. CITIZEN BOT (Public Facing)
- Clean, professional portal UI
- Bilingual: Bahasa Malaysia + English (auto-detect or manual toggle)
- Answers STRICTLY from public-tagged documents only
- Hardcoded access filter -- cannot be overridden by user input
- Prompt injection protection
- Rate limiting (10 requests per minute per IP)
- Graceful fallback message when answer not found
- Mobile responsive
- No authentication required

4. STAFF BOT (Internal)
- Secure login with JWT authentication
- Role-based access control: Clerk / Manager / Director / Admin
- Each role sees documents matching their clearance level
- Full conversation memory per session
- Report generation: user types natural language, system generates SQL, returns formatted data
- Export reports as PDF and Excel
- Audit log: every query logged immutably (user, timestamp, question, sources, response)
- Staff can view own logs; Directors can view all logs

5. LLM INTEGRATION
- Primary: OpenAI GPT-4o-mini via API/Any suggested by you
- Fallback option: local Ollama (Mistral/Llama) for air-gapped environments
- Temperature 0.0 for factual accuracy
- Strict document-only answering -- no hallucination from general knowledge
- Response must always cite source document and page number
- Support for streaming responses

6. SECURITY
- All data stays on-premise (no third-party data storage)
- PII never stored in vector database
- SQL injection protection on report generation
- Read-only database credentials for bot queries
- HTTPS enforcement in production
- Input sanitisation on all endpoints
- OWASP-compliant API design

7. INFRASTRUCTURE
- Docker + Docker Compose for all services
- Services: vector DB, citizen API, staff API, frontends, PostgreSQL, Redis cache
- Environment-based configuration (.env)
- Health check endpoints
- Horizontal scaling support
- Logging and monitoring hooks (compatible with Grafana/Prometheus)

8. TECH STACK (preferred, open to discussion)
- Backend: Python FastAPI or PHP (Laravel)
- Frontend: React.js
- Vector DB: Qdrant
- Relational DB: PostgreSQL
- Cache: Redis
- LLM: OpenAI API + Ollama fallback
- Embeddings: text-embedding-3-small (OpenAI) or nomic-embed-text (Ollama)
- Containerisation: Docker
- Auth: JWT

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DELIVERABLES
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- Complete source code (GitHub repo, clean commits)
- Citizen Bot -- fully functional frontend + API
- Staff Bot -- fully functional frontend + API with all role levels
- Ingestion pipeline script (handles 10,000+ docs)
- Docker Compose setup (one command to run everything)
- Database schema + migration scripts
- Comprehensive README with Windows + Linux setup instructions
- API documentation (Swagger/OpenAPI)
- Security audit report
- Performance benchmark (query response time with 100k+ vectors)
- 2 weeks post-delivery bug fix support

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IDEAL CANDIDATE
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- Strong experience with RAG architecture and vector databases
- Built production AI systems, not just demos or tutorials
- Familiar with Malaysian government context or similar multilingual government deployments
- Security-conscious -- understands data privacy requirements
- Can work independently and communicate clearly in English
- Portfolio of similar AI/chatbot projects required
- Experience with large-scale document processing (10k+ files) is a strong plus

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BUDGET AND TIMELINE
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Budget: Open to proposals -- please provide a detailed breakdown
Timeline: ASAP
Engagement: Fixed price preferred, milestone-based payments

Milestones suggested:
- Milestone 1 (20%): Ingestion pipeline + Qdrant setup working with 1,000 test docs
- Milestone 2 (50%): Both chatbot APIs functional with correct document-only answering
- Milestone 3 (20%): Both frontends complete, report generation, audit logging
- Milestone 4 (10%): Docker deployment, documentation, final testing

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TO APPLY
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Please include in your proposal:
1. A brief description of a similar RAG or document chatbot system you have built
2. Your recommended tech stack and any changes to the above
3. Estimated timeline and milestone breakdown
4. How you would handle 10,000+ document ingestion efficiently
5. Your approach to preventing the bot from answering outside the documents

Proposals without relevant experience will not be considered.

This is a serious, funded project for a client. We are looking for a professional who delivers production-quality work, not a proof of concept.

We are also willing to pay for a proof of concept first. Upon successful, we will pay for a full development.

I am seeking a skilled DevOps enthusiast to participate in a practice project focusing on AWS, Terraform, and container orchestration tools. we will decide project for practice involves setting up environments using Docker, managing infrastructure as code with Terraform, and automating processes with Ansible. Additionally, we will utilize Jenkins for CI/CD, Kubernetes for orchestration, and monitoring tools like Prometheus and Grafana. We will conduct two sessions per week, each lasting one hour, to collaboratively work on the project and enhance my DevOps skills.

Flexible Timing,just work like a mentor for practice session.we will try to complete level 1 project .

• Can you follow usa time zone? Can we decide on our flexible time?
• Describe your recent experience with similar projects

Python developer who is comfortable working across backend development, application integration, deployment, ML tooling, and general infrastructure support. Spanish-speaking is a plus!

Core technical skills:

- Strong Python development skills, including writing clean, maintainable, testable, and reusable code
- Experience refactoring Python codebases and improving code quality/structure
- Familiarity with backend development, including REST APIs, service integrations, and application scaffolding
- Experience with Streamlit for internal tools and lightweight application interfaces
- Familiarity with Grafana for monitoring and dashboards
- Experience with Airflow for workflow orchestration and scheduled pipelines
- Experience with MLflow for experiment tracking, model management, and deployment workflows

Systems and infrastructure:

- Comfortable working in Unix-based environments, especially Linux and macOS
- Strong command-line / terminal experience, including shell usage and Bash
- Familiarity with distributed systems concepts and server-side execution environments
- Experience with Docker for containerization
- Familiarity with Kubernetes for orchestration and deployment
- Experience troubleshooting runtime, environment, and dependency issues in server environments

Version control and engineering workflow:

- Strong Git skills, especially working in GitHub-based development workflows
- Familiarity with CI/CD pipelines for deployment and service integration
- Experience handling release/runtime secrets and environment configuration
- Ability to support team members with engineering workflow issues such as DevContainers, CI/CD, pre-commit hooks, environments, and dependency setup

Integration / platform skills:

- Experience integrating applications with external services
- Familiarity with SMTP-based integrations and email service workflows
- Familiarity with API-based systems and general service communication patterns
- Exposure to AWS environments and cloud-based deployment/infrastructure workflows

Machine learning and LLM:

- General knowledge of machine learning workflows and model deployment
- Familiarity with GPU-based environments and NVIDIA libraries/tooling
- Hands-on experience working with LLM-related libraries and frameworks
- It would be helpful if candidates can demonstrate prior work with libraries such as Transformers, vLLM, LiteLLM, sentence-transformers, LangChain, or similar tools in production or near-production settings

Type of work expected in the role:

- Building Python scaffolding and reusable utilities for applications
- Refactoring code written by data scientists into more robust production-quality components
- Developing backend services, APIs, and integrations
- Supporting dashboards and internal tools
- Setting up and maintaining CI/CD pipelines across services
- Managing execution environments, secrets, and deployment configuration
- Containerizing services and helping with orchestration
- Troubleshooting system, dependency, and infrastructure issues
- Supporting other team members when they run into tooling or environment problems

We are seeking a skilled developer to create software that ensures compliance with NERC standards. The ideal candidate will have experience in developing regulatory compliance solutions, particularly in the energy sector. Your role will involve designing, coding, and implementing software features that meet NERC requirements. Strong problem-solving skills and attention to detail are essential. If you have a background in compliance software development and a passion for the energy industry, we would love to hear from you!
Request for Proposal (RFP)
Development of NERC Compliance Management Software Platform
Issued By
Keentel Engineering
RFP Issue Date
3/12/2026
Proposal Submission Deadline
3/28/2026



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1. Introduction
Keentel Engineering is seeking proposals from qualified software development firms to design, develop, deploy, and maintain a comprehensive NERC Compliance Management Software Platform.
The platform will support compliance with NERC Reliability Standards, including but not limited to:
• PRC Standards
• CIP Standards
• FAC Standards
• TPL Standards
• MOD Standards
• BAL Standards
• IBR-related standards (PRC-029, PRC-024, etc.)
The solution must provide a centralized platform for compliance documentation, evidence management, monitoring, reporting, and audit readiness.
The platform must include:
• Desktop application
• Web portal
• iOS mobile application
• Android mobile application
The system must support engineering teams, compliance officers, auditors, and executives.
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2. Project Objectives
The goal is to develop a secure, scalable, modern compliance management system that enables:
• NERC compliance tracking
• Document management and evidence storage
• Automated compliance workflows
• Audit preparation and reporting
• Real-time compliance monitoring
• Integration with engineering tools and grid data platforms
The system should enable utilities, generator owners (GO), transmission owners (TO), and engineering firms to manage compliance obligations efficiently.
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3. Scope of Work
The selected vendor will be responsible for:
1. Software architecture design
2. UI/UX design
3. Frontend development
4. Backend development
5. Mobile app development
6. Database design
7. API development
8. Cybersecurity implementation
9. Cloud deployment
10. Testing and QA
11. Documentation
12. Maintenance and support
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4. Platform Architecture
The proposed system should use a modern scalable architecture.
Recommended architecture:
Cloud Native Architecture
Preferred cloud providers:
• AWS
• Microsoft Azure
• Google Cloud
Architecture should include:
• Microservices architecture
• RESTful APIs
• Event-driven architecture
• Containerized services (Docker)
• Kubernetes orchestration
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5. Frontend Requirements
Frontend must support desktop and web-based environments.
Technologies (Preferred)
• React.js
• Angular
• Vue.js
• TypeScript
• Next.js
UI/UX Requirements
The interface must include:
• Dashboard for compliance status
• Compliance standard tracking (PRC, CIP, FAC, etc.)
• Evidence upload and management
• Audit preparation modules
• Engineering data integration dashboards
• Role-based access interfaces
Features
• Responsive design
• Dark/light mode
• Advanced filtering
• Data visualization
• Compliance timeline tracking
• Drag-and-drop document uploads
Visualization Tools
Recommended libraries:
• D3.js
• Chart.js
• PowerBI embedded
• Grafana dashboards
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6. Mobile Applications
The solution must include native mobile apps.
Platforms
• iOS
• Android
Development Frameworks